where \(x^+\)  is a “positive” sample for \(x\) ,
and \(\mathcal {N}( x )\)  comprises contrasting “negative” samples for \(x\) .
In standard contrastive learning, there is no explicit supervision, and so \(\mathcal {N}( x )\)  may be taken to be randomly sampled inputs.
In supervised contrastive learning [1]}, [2]},
it is assumed that label information is present, and
\(\mathcal {N}( x )\)  comprises samples with a different label than \(x, x^+\) .
Such techniques do not involve a logit margin, and are not adapted to long-tail settings.
